Efficient Asset Management: A Practical Guide to Stock Portfolio Optimization and Asset Allocation

145 Pages·2008·1.97 MB·English

by  Richard O. Michaud

EFFICIENT ASSET MANAGEMENT
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EFFICIENT ASSET MANAGEMENT
A Practical Guide to Stock Portfolio Optimization
and Asset Allocation
Second Edition
By Richard O. Michaud and Robert O. Michaud
1
2008
1
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Library of Congress Cataloging-in-Publication Data
Michaud, Richard O., 1941–
Effi cient asset management: a practical guide to stock portfolio optimization
and asset allocation / Richard O. Michaud and Robert O. Michaud.—2nd ed.
p. cm.—(Financial management association survey and synthesis series)
Includes bibliographical references (p. ) and index.
ISBN 978-0-19-533191-2
1. Investment analysis—Mathematical
models. 2. Portfolio management—Mathematical models.
I. Michaud, Robert O. \ II. Title.
HG4529.M53 2008
332.6—dc22 2007020912
9 8 7 6 5 4 3 2 1
Printed in the United States of America on acid-free paper
To
My mother, Helena Talbot Michaud, and her steadfast love
My father, Omer Michaud, and his cherished memory
Prof. Robin Esch, a wise, unerring mentor
Drs. Allan Pineda, John Levinson, and Cary Atkins
Richard Michaud, 2007
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Preface
Effective asset management is not only a matter of identifying desirable
investments: it also requires optimally structuring the assets within the
portfolio. This is because the investment behavior of a portfolio is typi-
cally different from the assets in it. For example, the risk of a portfolio of
U.S. equities is often half the average risk of the stocks in it.
Prudent investors concern themselves with portfolio risk and return.
An understanding of effi cient portfolio structure is essential for opti-
mally managing the investment benefi ts of portfolios. Effective portfolio
management reduces risk while enhancing return. For thoughtful inves-
tors, portfolio effi ciency is no less important than estimating risk and
return of assets.
Most institutional investors and fi nancial economists acknowledge
the investment benefi ts of effi cient portfolio diversifi cation. Optimally
managing portfolio risk is an essential component of modern asset man-
agement. Markowitz (1959, 1987) gave the classic defi nition of portfolio
optimality: a portfolio is effi cient if it has the highest expected (mean or
estimated) return for a given level of risk (variance) or, equivalently, least
risk for a given level of expected return of all portfolios from a given uni-
verse of securities. Markowitz mean-variance (MV) effi ciency is a practi-
cal and convenient framework for defi ning portfolio optimality and for
constructing optimal stock portfolios and asset allocations. A number of
commercial services provide optimizer software for computing MV effi -
cient portfolios.
viii Preface
INVESTOR ACCEPTANCE
Modern asset management typically employs many theoretical fi nancial
concepts and advanced analytical techniques. Perhaps the most outstand-
ing example is in the management of derivative instruments. Within a
few years of the publication of seminal papers (Black & Scholes, 1973;
Merton, 1973) and the opening of derivative exchanges, an extensive indus-
try applying quantitative techniques to derivative strategies emerged. In
a similar fashion, many fi xed income managers use sophisticated port-
folio structuring techniques for cash fl ow liability management.1 In con-
trast, many institutional equity managers do not use MV optimizers to
structure portfolios.
The relatively low level of analytical sophistication in the culture of
institutional equity management is one often-cited reason for the lack
of acceptance of MV optimization, along with organizational and politi-
cal issues. The investment policy committee and an optimizer perform
essentially the same integrative investment function. Consequently, the
fi rm’s senior investment offi cers may view an optimizer, and the quanti-
tative specialist who manages it, as usurping their roles and challenging
their control and political power in the organization.
Despite these reasons, it is hard to imagine why investment managers
do not behave in their best interests as well as those of their clients. Expe-
rience in derivatives and fi xed income management demonstrates that the
investment community quickly adopts highly sophisticated analytics and
computer technology when provably useful. If cultural, political, or com-
petence factors limit the use of MV optimizers in traditional investment
organizations, new fi rms should form without these limitations, with
the objective of leveraging the technology and dominating the industry.
Indeed, many “quantitative” equity management fi rms, formed over the
past 35 years, have this objective. However, the “Markowitz optimiza-
tion enigma”—the fact that many traditional equity managers ignore MV
optimization—can be largely explained without recourse to irrationality,
incompetence, or politics (Michaud, 1989a). The basic problem is that MV
portfolio effi ciency has fundamental investment limitations as a practical
tool of asset management. It is likely that the limitations of MV optimiz-
ers have been an important factor in limiting the success of many quanti-
tative equity managers relative to their more traditional competitors.
THE FUNDAMENTAL ISSUE
Although Markowitz effi ciency is a convenient and useful theoretical
framework for defi ning portfolio optimality, in practice it is a highly error-
prone and unstable procedure that often results in “error maximized” and
1. Liebowitz (1986) describes some of these techniques.
Preface ix
“investment irrelevant” portfolios (Jobson & Korkie, 1980, 1981; Michaud,
1989a). Proposed alternative optimization technologies share similar, if
not even more signifi cant, limitations. MV effi ciency limitations in prac-
tice generally derive from a lack of statistical understanding of the MV
optimization process. A “statistical” view of MV optimization leads to
new procedures that eliminate the most serious defi ciencies for many
practical applications. Statistical MV optimization may enhance invest-
ment value while providing a more intuitive framework for asset man-
agement. A statistical view also challenges and corrects many current
practices for optimized portfolio management.
OVERVIEW
This book describes the problems associated with MV optimization as a
practical tool of asset management and provides resolutions that refl ect
its essential, though often neglected, statistical character. A review of
proposed alternatives of MV optimization is given and their theoretical
and practical limitations are noted. A “statistical” perspective serves as
a valuable route for the development and application of powerful tech-
niques that enhance the practical value of MV optimized portfolios.
The goal is to conserve the many benefi ts of traditional MV optimiza-
tion while enhancing investment effectiveness and avoiding its rigidity.
New tools are developed that enable an intuitive effective framework for
meeting the demand characteristics from institutional asset managers to
sophisticated fi nancial advisors and investors. A simple asset allocation
example illustrates the issues and new procedures. The text maintains a
practical perspective throughout.
The second edition is extensively revised. Chapters 7 and 9 are nearly
completely rewritten with new techniques, research, and expanded
scope. Chapters 4, 5, 6, 8, 10, and 11 are extensively revised. The remain-
ing chapters have also been updated.
The new reader will fi nd a rich investment-practice–informed set of
ideas, while the reader of the fi rst edition will fi nd extensive new mate-
rial, including expansion of scope as well as development of earlier ideas.
The new edition benefi ts from nearly 7 years of the authors’ experience
applying the technology to a wide spectrum of practical investment
needs, including those of institutional asset managers, investment strat-
egists, high-net-worth advisors, institutional consultants, and fi nancial
advisors worldwide. The authors also have nearly 3 years of actual asset
management using the technology with favorable results.
FEATURES
This text is the fi rst to integrate and systematically treat practical MV
optimization from a statistical, rather than a numerical, point of view.